Trading with Time Series Causal Discovery: An Empirical Study

2024/08/28 by Ruijie Tang, Tang, Ruijie
Computer Science · #Time Series Analysis and Forecasting #Bayesian Modeling and Causal Inference #Advanced Database Systems and Queries

paper · pdf · doi:10.48550/arxiv.2408.15846

Abstract

This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis.

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